I had a chance to speak at the Cloud Conf 2019 in Turin, Italy. The conference has double its audience from last year, had a spectacular venue, and large selection of topics. I spoke in the #serverless track on using Knative as a means to serverless where you want it and on your own terms.
I wrote a new post on Google blog on the momentum behind the Knative project. How it the community reached another adoption milestone, doubling the number of its contributors. Also, another data point underscoring the Knative momentum is the month-over-month contributions which have increased over 45% since the 0.
I had an opportunity to keynote at this year’s SpringOne conference in DC on Serverless, Kubernetes, and more specifically Knative. I also covered the great work our open source team at Google been doing, making Spring 1st class citizen on Google Cloud Platform.
Ville and I did a session at Google Cloud Next 2018 in San Francisco. I also published the slides as well as the repo containing all the demos I used in this session in my repo here.
By now, Kubernetes should be the default target for your deployments. Yes, there are still use-cases where Kubernetes is not the optimal choice, but these represent an increasingly smaller number of modern workloads. The main value of Kubernetes is that it greatly abstracts much of the infrastructure management pain.
Google Stackdriver has thousands of build-in metrics to monitor everything from Kubernetes cluster to database or storage. Stackdriver is also not just limited to Google Cloud Platform (GCP), it supports a number of AWS-native services and extensive log monitoring capabilities for a wide array of open source software packages, whether they run in the Cloud or in on premises.
I wanted to use the now generally available Cloud Spanner database to write an app that would track stock prices and social media sentiment to identify potential correlation. To test even the validity of this approach I put together a Go app that subscribes to Twitter stream for all companies defined in the Stocks table and scores each event against the Google NLP API while comparing the user sentiment against the stock ask price against Yahoo API.
As part of my ramp up on Google APIs I wanted to create a project that would allow me some practical exercise in a context of a real application. TFeel (short for Twitter Feeling) is a simple sentiment analyses over tweeter data for specific Twitter search terms using Google Cloud services:
I had the opportunity to attend Google Next this year. Week after this event I joined Google. Here are some quick notes in no particular order: Registration was a pain, long lines. My first tech conference where I had to go through a metal detector.
Data is growing at an exponential pace. Based on recent numbers from IDC, the total amount of data in 2015 (4.4ZB) will grow to 44ZB in 2020. Franky, how much is in Zettabyte is almost inconsequential. It is the fact that all of the data generated since the beginning of time (at least the electronic part), will grow 10x in just the next four years that’s shocking!